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Xentity is Architecting mission critical systems with heavy data workflow and GIS applications, services, and modules. The team is creating RFP Requirements for major implementation including analyzing entire program GIS needs, work products raster, vector, and cartographic product needs; Analyzing enterprise architecture and infrastructure to support plan; Designing specification, requirements, schedule, scope, architecture, and implementation plan; Enumerating workflows, process, data and screens. Xentity has trained a prime team on architecture, data management, and GIS concepts to include and integrate into non-GIS components and workflow.

Problem and Summary

The MLRS investment is designed to provide the data and functionality needed to give BLM land managers state-of-the-art tools needed to make informed land use decisions that will withstand public and legal scrutiny. Prior to Xentity’s aid, this effort has undergone multiple studies to explore the feasibility of a comprehensive redesign and implementation of its existing mineral, land, and related record systems that would result in a single Bureau-wide system incorporating the latest advancements in data systems and computer technology including national and state system re-use (Alaska and Oregon). 

Xentity was contracted to provide support for the prime BLM SETA MLRS Planning Phase Support PWS Description of Services, in accordance with Vistronix Project Manager direction and Consultant’s proposal. The objective of the Minerals and Lands Reporting System (MLRS) project is the development of a modern system employing automated business flows, that will enable the storage and retrieval of public land and mineral records which document the rights and interest of the U.S. in the form of electronic documents and metadata to include geospatial references.


We provided an MLRS requirements definition. Also, requirements traceability matrix, process flow charts, evaluation reports, data architecture models, data inventory, mapping and registry. Our efforts also provided comprehensive data coverage. Our solutions accounted for all US rights, title, interests, uses, and restrictions. Accurately provided data that allows users to query and derive tabular and spatial land status information (both current and historical). All federal lands and minerals, including public domain, were represented. Furthermore, we provided data quality and integrity improvement. We designed a system for portfolio integration, case auditing and land status tracking. Finally, we provided improved publication and reporting. Our design supported data services such as query, reporting, mapping, publishing and document linking.

Outcome and Benefit

Xentity’s efforts developed assessment, requirements, and CONOPS that integrate energy and minerals full lifecycle. This included planning, analysis, permitting, leasing, spatial workflow analysis, National Environmental Policy Act (NEPA), compliance. These covered the scope of all fluids and solids for energy and non-energy for all of BLM across national, state, and field offices for over 247 million acres of federal surface estate and 700 million acres of subsurface mineral estate. This is a challenge that has been at work since the early 1990s for BLM. Also, the recently developed CONOPS ions and operation model delivered has been reviewed by the key stakeholders, and steering governance. This includes the overall energy and minerals Program Senior Management, and BLM Chief Information Office (CIO).

Other Highlights

  • The AI POC extracts, classifies, then supports queries on common, rules, conflicts between BLM with an estimation of 40k docs
    • It does the same for Geo Features for Local datasets where no standards in attribution to help in finding common patterns.
    • It also extracts from Document Geo references to then generate the map of impacted polygons and adds document attributes for maps.
  • It’s pretty powerful for NEPA, Minerals Land Records, and Resource Management Planning. Also, in dealing with lack of any standardization in unstructured documents and local GIS data.
  • It’s primarily NLP, Geo Classification. It’s for facilitating discovery of conflicts – not for suggestions of what to do with rules. 
  • There is no Computer Vision for image feature extraction in phase 1 from drones, embedded imagery, in documents or supporting GeoPDFs, etc. That was backlogged by BLM for now.
  • AI capabilities using ElasticSearch, Python, API integration including BLM Land Management Doc Rules Discovery, Geospatial.
  • Whether for demonstrating for Land Management purposes, or generally demonstrating the concepts of NLP/Geo classification advancement (e.g. could be on AWS EMR) as a GP capability.